One definition of Artificial Intelligence (AI) is everything a computer can't do yet – because as soon as it can do it, we call it normal.
Behind that wry remark are the remarkable quiet advance computing IQ has been making over the years. Many complex, abstract capabilities thought to be exclusive to the human mind are no longer so – from winning TV game shows to recognising faces or driving
cars, machines have relentlessly expanded the range of activities in which they can match or even outperform us.
That’s happened thanks to rapid increases in computing power combined with big leaps forward in learning and pattern recognition algorithms. Computers can now see, listen, read, and write, but also understand and give meaning – and in everyday contexts,
not in remote AI Labs. Skype can translate conversations in real time, natural language processing is used by applications like Siri, Google Now or Cortana – a ubiquity that makes interactions with clever software more natural and pervasive.
But computers can now do more than interact with humans. Virtual assistants help experts in aeronautics or oil-platforms to perform complex constructions and reparations; while a system called Amelia can digest an oil-well centrifugal-pump manual in half
a minute, give instructions for repairs, plus act as a call centre operative, mortgage and insurance agent. IBM’s AI system Watson has already contributed to cancer research.
To achieve these feats, the underlying software brain is not programed to cover every possible situation (you cannot generate and store the translation of
every possible sentence in every language). Instead, this new generation of AI is able to continually learn, freeing it from the constraints that tended to hold it back in previous decades.
Why financial services?
The financial sector naturally lends itself to the use of cognitive technologies: the complexity of the trading markets, the vast amount of data involved, the need for automation and better customer experience make cognitive technologies a convincing solution.
In risk management and compliance, for example, smart agents could easily evaluate all cases against approved policies and guidelines and understand the complexities of risk exposure. Financial and market analysis could be made much sharper through the analysis
of vast amount of information. And in sharp contrast to traditional analytics, smart agents are capable, thanks to that built-in learning philosophy, of detecting key trends and variables thst even the smartest ‘quants’ may miss.
Today, in wealth management, relationship managers advise their clients by analysing large volumes of complex data such as research reports, product information, and customer profiles. How big a step is it before smart advisors could do the same? In fact,
systems like Watson and Amelia are already used by top financial institutions: DBS Bank uses the former to advise wealth management customers, while one of the biggest US banks uses the latter to manage trading platforms and call centres.
Clearly, we have now reached the tipping point where cognitive technology capabilities are powerful and reliable enough to be deployed in real life environments.
The good news is for those fearing an era of de-skilling and mass unemployment the human presence will always stay essential. Some activities will be mostly automated (e.g., call centres). Other activities like investment advice will be conducted by human
professionals, albeit enhanced by cognitive technologies.
Now is the time to take the opportunity to share the workplace with the robots.